Data Mining:
Concepts and Techniques
— Chapter 3 —
Jiawei Han
Department of Computer Science
University of Illinois at Urbana-Champaign www.cs.uiuc.edu/~hanj
©2006 Jiawei Han and Micheline Kamber, All rights reserved
Chapter 3: Data Warehousing and OLAP Technology: An Overview
What is a data warehouse?
A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
From data warehousing to data mining
What is Data Warehouse?
Defined in many different ways, but not rigorously.
A decision support database that is maintained separately from the organization’s operational database
Support information processing by providing a solid platform of consolidated, historical data for analysis.
“A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s
decision-making process.”—W. H. Inmon
Data warehousing:
The process of constructing and using data warehouses
Data Warehouse—Subject-Oriented
Organized around major subjects, such as customer, product, sales
Focusing on the modeling and analysis of data for
decision makers, not on daily operations or transaction processing
Provide a simple and concise view around particular
subject issues by excluding data that are not useful in
the decision support process
Data Warehouse—Integrated
Constructed by integrating multiple, heterogeneous data sources
relational databases, flat files, on-line transaction records
Data cleaning and data integration techniques are applied.
Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources
E.g., Hotel price: currency, tax, breakfast covered, etc.
When data is moved to the warehouse, it is
converted.
Data Warehouse—Time Variant
The time horizon for the data warehouse is significantly longer than that of operational systems
Operational database: current value data
Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years)
Every key structure in the data warehouse
Contains an element of time, explicitly or implicitly
But the key of operational data may or may not
contain “time element”
Data Warehouse—Nonvolatile
A physically separate store of data transformed from the operational environment
Operational update of data does not occur in the data warehouse environment
Does not require transaction processing, recovery, and concurrency control mechanisms
Requires only two operations in data accessing:
initial loading of data and access of data
Data Warehouse vs. Heterogeneous DBMS
Traditional heterogeneous DB integration: A query driven approach
Build wrappers/mediators on top of heterogeneous databases
When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual
heterogeneous sites involved, and the results are integrated into a global answer set
Complex information filtering, compete for resources
Data warehouse: update-driven, high performance
Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis
Data Warehouse vs. Operational DBMS
OLTP (on-line transaction processing)
Major task of traditional relational DBMS
Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.
OLAP (on-line analytical processing)
Major task of data warehouse system
Data analysis and decision making
Distinct features (OLTP vs. OLAP):
User and system orientation: customer vs. market
Data contents: current, detailed vs. historical, consolidated
Database design: ER + application vs. star + subject
View: current, local vs. evolutionary, integrated
Access patterns: update vs. read-only but complex queries
OLTP vs. OLAP
OLTP OLAP
users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date
detailed, flat relational isolated
historical,
summarized, multidimensional integrated, consolidated
usage repetitive ad-hoc
access read/write
index/hash on prim. key
lots of scans
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
metric transaction throughput query throughput, response
Why Separate Data Warehouse?
High performance for both systems
DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery
Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation
Different functions and different data:
missing data: Decision support requires historical data which operational DBs do not typically maintain
data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources
data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled
Note: There are more and more systems which perform OLAP analysis directly on relational databases
Chapter 3: Data Warehousing and OLAP Technology: An Overview
What is a data warehouse?
A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
From data warehousing to data mining
From Tables and Spreadsheets to Data Cubes
A data warehouse is based on a multidimensional data model which views data in the form of a data cube
A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions
Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year)
Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables
In data warehousing literature, an n-D base cube is called a base cuboid. The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid. The lattice of cuboids forms a data cube.
Cube: A Lattice of Cuboids
time,item
time,item,location
time, item, location, supplier
all
time item location supplier
time,location
time,supplier
item,location
item,supplier
location,supplier
time,item,supplier
time,location,supplier
item,location,supplier
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D cuboids
4-D(base) cuboid
Conceptual Modeling of Data Warehouses
Modeling data warehouses: dimensions & measures
Star schema: A fact table in the middle connected to a set of dimension tables
Snowflake schema: A refinement of star schema
where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape
similar to snowflake
Fact constellations: Multiple fact tables share
dimension tables, viewed as a collection of stars,
therefore called galaxy schema or fact constellation
Example of Star Schema
time_key day
day_of_the_week month
quarter year
time
location_key street
city
state_or_province country
location Sales Fact Table
time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures
item_key item_name brand
type
supplier_type
item
branch_key branch_name branch_type
branch
Example of Snowflake Schema
time_key day
day_of_the_week month
quarter year
time
location_key street
city_key
location Sales Fact Table
time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures
item_key item_name brand
type
supplier_key
item
branch_key branch_name branch_type
branch
supplier_key supplier_type
supplier
city_key city
state_or_province country
city
Example of Fact Constellation
time_key day
day_of_the_week month
quarter year
time
location_key street
city
province_or_state country
location Sales Fact Table
time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures
item_key item_name brand
type
supplier_type
item
branch_key branch_name branch_type
branch
Shipping Fact Table time_key
item_key shipper_key from_location to_location dollars_cost units_shipped
shipper_key shipper_name
shipper
Cube Definition Syntax (BNF) in DMQL
Cube Definition (Fact Table)
define cube <cube_name> [<dimension_list>]:
<measure_list>
Dimension Definition (Dimension Table) define dimension <dimension_name> as
(<attribute_or_subdimension_list>)
Special Case (Shared Dimension Tables)
First time as “cube definition”
define dimension <dimension_name> as
<dimension_name_first_time> in cube
<cube_name_first_time>
Defining Star Schema in DMQL
define cube sales_star [time, item, branch, location]:
dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*)
define dimension time as (time_key, day, day_of_week, month, quarter, year)
define dimension item as (item_key, item_name, brand, type, supplier_type)
define dimension branch as (branch_key, branch_name, branch_type)
define dimension location as (location_key, street, city,
province_or_state, country)
Defining Snowflake Schema in DMQL
define cube sales_snowflake [time, item, branch, location]:
dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*)
define dimension time as (time_key, day, day_of_week, month, quarter, year)
define dimension item as (item_key, item_name, brand, type, supplier(supplier_key, supplier_type))
define dimension branch as (branch_key, branch_name, branch_type) define dimension location as (location_key, street, city(city_key,
province_or_state, country))
Defining Fact Constellation in DMQL
define cube sales [time, item, branch, location]:
dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*)
define dimension time as (time_key, day, day_of_week, month, quarter, year) define dimension item as (item_key, item_name, brand, type, supplier_type) define dimension branch as (branch_key, branch_name, branch_type)
define dimension location as (location_key, street, city, province_or_state, country)
define cube shipping [time, item, shipper, from_location, to_location]:
dollar_cost = sum(cost_in_dollars), unit_shipped = count(*) define dimension time as time in cube sales
define dimension item as item in cube sales
define dimension shipper as (shipper_key, shipper_name, location as location in cube sales, shipper_type)
define dimension from_location as location in cube sales define dimension to_location as location in cube sales
Measures of Data Cube: Three Categories
Distributive: if the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning
E.g., count(), sum(), min(), max()
Algebraic: if it can be computed by an algebraic function with M arguments (where M is a bounded integer), each of which is obtained by applying a distributive aggregate
function
E.g., avg(), min_N(), standard_deviation()
Holistic: if there is no constant bound on the storage size needed to describe a subaggregate.
E.g., median(), mode(), rank()
A Concept Hierarchy: Dimension (location)
all
Europe North_America
Mexico Canada
Spain Germany
Vancouver
M. Wind L. Chan
...
...
...
... ...
...
all region
office country
Toronto Frankfurt
city
View of Warehouses and Hierarchies
Specification of hierarchies
Schema hierarchy day < {month <
quarter; week} < year
Set_grouping hierarchy
{1..10} < inexpensive
Multidimensional Data
Sales volume as a function of product, month, and region
P ro du ct
Re gi on
Month
Dimensions: Product, Location, Time Hierarchical summarization paths
Industry Region Year Category Country Quarter
Product City Month Week Office Day
A Sample Data Cube
Total annual sales of TV in U.S.A.
Date Pr od uc t
C ou n tr y
sum
TV sum VCRPC
1Qtr 2Qtr 3Qtr 4Qtr
U.S.A
Canada Mexico
sum
Cuboids Corresponding to the Cube
all
product date country
product,date product,country date, country
product, date, country
0-D(apex) cuboid 1-D cuboids
2-D cuboids
3-D(base) cuboid
Browsing a Data Cube
Visualization
OLAP capabilities
Interactive manipulation
Typical OLAP Operations
Roll up (drill-up): summarize data
by climbing up hierarchy or by dimension reduction
Drill down (roll down): reverse of roll-up
from higher level summary to lower level summary or detailed data, or introducing new dimensions
Slice and dice:
project and select
Pivot (rotate):
reorient the cube, visualization, 3D to series of 2D planes
Other operations
drill across: involving (across) more than one fact table
drill through: through the bottom level of the cube to its
back-end relational tables (using SQL)
Fig. 3.10 Typical OLAP Operations
A Star-Net Query Model
Shipping Method
AIR-EXPRESS
TRUCK ORDER
Customer Orders
CONTRACTS
Customer
Product PRODUCT GROUP PRODUCT LINE
PRODUCT ITEM
SALES PERSON DISTRICT
DIVISION CITY
COUNTRY
REGION Location
DAILY QTRLY
ANNUALY Time
Each circle is
Chapter 3: Data Warehousing and OLAP Technology: An Overview
What is a data warehouse?
A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
From data warehousing to data mining
Design of Data Warehouse: A Business Analysis Framework
Four views regarding the design of a data warehouse
Top-down view
allows selection of the relevant information necessary for the data warehouse
Data source view
exposes the information being captured, stored, and managed by operational systems
Data warehouse view
consists of fact tables and dimension tables
Business query view
sees the perspectives of data in the warehouse from the view of end-user
Data Warehouse Design Process
Top-down, bottom-up approaches or a combination of both
Top-down: Starts with overall design and planning (mature)
Bottom-up: Starts with experiments and prototypes (rapid)
From software engineering point of view
Waterfall: structured and systematic analysis at each step before proceeding to the next
Spiral: rapid generation of increasingly functional systems, short turn around time, quick turn around
Typical data warehouse design process
Choose a business process to model, e.g., orders, invoices, etc.
Choose the grain (atomic level of data) of the business process
Choose the dimensions that will apply to each fact table record
Choose the measure that will populate each fact table record
Data Warehouse: A Multi-Tiered Architecture Data Warehouse: A Multi-Tiered Architecture
Data Warehouse
Extract Transform Load
Refresh
OLAP Engine
Analysis Query Reports
Data mining
Monitor
&
Integrator
Metadata
Data Sources Front-End Tools
Serve
Data Marts
Operational DBs
Other sources
Data Storage
OLAP Server
Three Data Warehouse Models
Enterprise warehouse
collects all of the information about subjects spanning the entire organization
Data Mart
a subset of corporate-wide data that is of value to a specific groups of users. Its scope is confined to
specific, selected groups, such as marketing data mart
Independent vs. dependent (directly from warehouse) data mart
Virtual warehouse
A set of views over operational databases
Only some of the possible summary views may be
materialized
Data Warehouse Development:
A Recommended Approach
Define a high-level corporate data model
Data Mart
Data Mart Distributed Data Marts
Multi-Tier Data Warehouse
Enterprise Data
Warehouse
Model refinement Model refinement
Data Warehouse Back-End Tools and Utilities
Data extraction
get data from multiple, heterogeneous, and external sources
Data cleaning
detect errors in the data and rectify them when possible
Data transformation
convert data from legacy or host format to warehouse format
Load
sort, summarize, consolidate, compute views, check integrity, and build indicies and partitions
Refresh
propagate the updates from the data sources to the
warehouse
Metadata Repository
Meta data is the data defining warehouse objects. It stores:
Description of the structure of the data warehouse
schema, view, dimensions, hierarchies, derived data defn, data mart locations and contents
Operational meta-data
data lineage (history of migrated data and transformation path), currency of data (active, archived, or purged), monitoring
information (warehouse usage statistics, error reports, audit trails)
The algorithms used for summarization
The mapping from operational environment to the data warehouse
Data related to system performance
warehouse schema, view and derived data definitions
Business data
business terms and definitions, ownership of data, charging policies
OLAP Server Architectures
Relational OLAP (ROLAP)
Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware
Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services
Greater scalability
Multidimensional OLAP (MOLAP)
Sparse array-based multidimensional storage engine
Fast indexing to pre-computed summarized data
Hybrid OLAP (HOLAP) (e.g., Microsoft SQLServer)
Flexibility, e.g., low level: relational, high-level: array
Specialized SQL servers (e.g., Redbricks)
Specialized support for SQL queries over star/snowflake schemas
Chapter 3: Data Warehousing and OLAP Technology: An Overview
What is a data warehouse?
A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
From data warehousing to data mining
Efficient Data Cube Computation
Data cube can be viewed as a lattice of cuboids
The bottom-most cuboid is the base cuboid
The top-most cuboid (apex) contains only one cell
How many cuboids in an n-dimensional cube with L levels?
Materialization of data cube
Materialize every (cuboid) (full materialization), none (no materialization), or some (partial materialization)
Selection of which cuboids to materialize
Based on size, sharing, access frequency, etc.
) 1( 1
n
i Li T
Cube Operation
Cube definition and computation in DMQL
define cube sales[item, city, year]: sum(sales_in_dollars) compute cube sales
Transform it into a SQL-like language (with a new operator cube by, introduced by Gray et al.’96)
SELECT item, city, year, SUM (amount) FROM SALES
CUBE BY item, city, year
Need compute the following Group-Bys (date, product, customer),
(date,product),(date, customer), (product, customer), (date), (product), (customer)
()
(item) (city)
()
(year)
(city, item) (city, year) (item, year)
(city, item, year)
Iceberg Cube
Computing only the cuboid cells whose count or other aggregates satisfying the condition like
HAVING COUNT(*) >= minsup
Motivation
Only a small portion of cube cells may be “above the water’’ in a sparse cube
Only calculate “interesting” cells—data above certain threshold
Avoid explosive growth of the cube
Suppose 100 dimensions, only 1 base cell. How many aggregate cells if count >= 1? What about count >= 2?
Indexing OLAP Data: Bitmap Index
Index on a particular column
Each value in the column has a bit vector: bit-op is fast
The length of the bit vector: # of records in the base table
The i-th bit is set if the i-th row of the base table has the value for the indexed column
not suitable for high cardinality domains
Cust Region Type C1 Asia Retail C2 Europe Dealer C3 Asia Dealer C4 America Retail C5 Europe Dealer
RecID Retail Dealer
1 1 0
2 0 1
3 0 1
4 1 0
5 0 1
RecID Asia Europe Am erica
1 1 0 0
2 0 1 0
3 1 0 0
4 0 0 1
5 0 1 0
Base table Index on Region Index on Type
Indexing OLAP Data: Join Indices
Join index: JI(R-id, S-id) where R (R-id, …) S (S-id, …)
Traditional indices map the values to a list of record ids
It materializes relational join in JI file and speeds up relational join
In data warehouses, join index relates the values of the dimensions of a start schema to rows in the fact table.
E.g. fact table: Sales and two dimensions city and product
A join index on city maintains for each distinct city a list of R-IDs of the tuples recording the Sales in the city
Join indices can span multiple dimensions
Efficient Processing OLAP Queries
Determine which operations should be performed on the available cuboids
Transform drill, roll, etc. into corresponding SQL and/or OLAP operations, e.g., dice = selection + projection
Determine which materialized cuboid(s) should be selected for OLAP op.
Let the query to be processed be on {brand, province_or_state} with the condition “year = 2004”, and there are 4 materialized cuboids available:
1) {year, item_name, city}
2) {year, brand, country}
3) {year, brand, province_or_state}
4) {item_name, province_or_state} where year = 2004 Which should be selected to process the query?
Explore indexing structures and compressed vs. dense array structs in MOLAP
Chapter 3: Data Warehousing and OLAP Technology: An Overview
What is a data warehouse?
A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
From data warehousing to data mining
Data Warehouse Usage
Three kinds of data warehouse applications
Information processing
supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs
Analytical processing
multidimensional analysis of data warehouse data
supports basic OLAP operations, slice-dice, drilling, pivoting
Data mining
knowledge discovery from hidden patterns
supports associations, constructing analytical models,
performing classification and prediction, and presenting the mining results using visualization tools
From On-Line Analytical Processing (OLAP) to On Line Analytical Mining (OLAM)
Why online analytical mining?
High quality of data in data warehouses
DW contains integrated, consistent, cleaned data
Available information processing structure surrounding data warehouses
ODBC, OLEDB, Web accessing, service facilities, reporting and OLAP tools
OLAP-based exploratory data analysis
Mining with drilling, dicing, pivoting, etc.
On-line selection of data mining functions
Integration and swapping of multiple mining
functions, algorithms, and tasks
An OLAM System Architecture
Data Warehouse
Meta Data
MDDB OLAM
Engine
OLAP Engine
User GUI API
Data Cube API
Database API
Data cleaning
Layer3 OLAP/OLAM
Layer2 MDDB
Layer1 Data Layer4 User Interface
Filtering&Integration Filtering
Databases
Mining query Mining result
Chapter 3: Data Warehousing and OLAP Technology: An Overview
What is a data warehouse?
A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
From data warehousing to data mining
Summary
Summary: Data Warehouse and OLAP Technology
Why data warehousing?
A multi-dimensional model of a data warehouse
Star schema, snowflake schema, fact constellations
A data cube consists of dimensions & measures
OLAP operations: drilling, rolling, slicing, dicing and pivoting
Data warehouse architecture
OLAP servers: ROLAP, MOLAP, HOLAP
Efficient computation of data cubes
Partial vs. full vs. no materialization
Indexing OALP data: Bitmap index and join index
OLAP query processing
From OLAP to OLAM (on-line analytical mining)
References (I)
S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. VLDB’96
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R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. ICDE’97
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ACM SIGMOD Record, 26:65-74, 1997
E. F. Codd, S. B. Codd, and C. T. Salley. Beyond decision support. Computer World, 27, July 1993.
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SIGMOD’96
References (II)
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OLAP council. MDAPI specification version 2.0. In http://www.olapcouncil.org/research/apily.htm, 1998
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